# Random

The Random Node.

The

**Random****Node**generates a random outcome, usually a number.This

**Node**can be set to three different`Modes`

(**Advanced**,**Expert**, and**Standard**). Each of these`Modes`

offers a different set of **Attributes**that are explained below.The Random Node Attributes.

This

`Mode`

allows the user to choose whether the random generator is deterministic or not, and for the deterministic case, the seed to use.Attribute | Type | Description |
---|---|---|

`Is Deterministic` | Bool | Whether the random generator is deterministic or not. |

`Seed` | Int (only available when
is set to true) | The `Seed` to use for the deterministic random generator. |

This

**Mode**has a**Drop-down**menu from which the*probability distribution*used for the random generator can be chosen. Each option offers its own set of**Attributes**with the*probability distribution*parameters.Attribute | Type | Description |
---|---|---|

`Distribution` | Drop-down | The probability distribution that the random generator will use. |

Next, the

**Attributes**for each*probability distribution*are described. For each*probability distribution*, the link to its corresponding Wikipedia entry is given.*Probability distribution*of a

*random variable*that can take two values:

*true*, with probability p; and

*false*, with probability 1-p. When this distribution is chosen, the outcome of the

**Node**is a

**Boolean**.

Attribute | Type | Description |
---|---|---|

`Probability of 'true'` | Float (between 0 and 1) | The probability that the outcome will be true. |

*Probability distribution*of the number of successes in a sequence of independent experiments, each one with two possible outcomes: success and failure. The parameters for this

*probability distribution*are the number of experiments and the probability of a successful outcome in each one.

Attribute | Type | Description |
---|---|---|

`Data Type` | Drop-down | Whether the outcome will be an Int or Byte. |

`Probability of 'true'` | Float | The probability that the outcome of each trial is true. |

`Number of trials` | Int | The number of independent experiments, each one with probability of success `Probability of 'true'` . |

Symmetric

*probability distribution*, with half its values less than the mean and half greater than the mean. The parameters are the mean, which equals the median and the mode, and the standard deviation.Attribute | Type | Description |
---|---|---|

`Mean` | Float | The mean value of the distribution. |

`Standard deviation` | Float | The standard deviation of the distribution. |

Discrete

*probability distribution*that expresses the probability of a given number of events occurring in a specified time period. Its parameter is the mean value.Attribute | Type | Description |
---|---|---|

`Data Type` | Drop-down | Wheter the outcome will be an Int or Byte. |

`Mean` | Float | The mean value of the distribution. |

- Uniform

*Probability distribution*in which all the values in an interval are equally likely to be drawn. It can either be continuous or discrete.

Attribute | Type | Description |
---|---|---|

`Data Type` | Drop-down | Whether an Int, Float, or Byte will be generated. |

`Minimum` | Defined in the
Attribute | The lower bound of the interval from which the random number will be extracted. |

`Maximum` | Defined in the
Attribute | The upper bound of the interval from which the random number will be extracted. |

This

`Mode`

allows to choose from a list of several types of random generators.Attribute | Type | Description |
---|---|---|

`Generator` | Drop-down | The type of random generator to use. |

`Seed` | Int (not available for non_deterministic ) | The `Seed` to use for the random generator. |

This

`Mode`

has a **Drop-down**menu from which the*probability distribution*to be used for the random generator can be chosen. Each option offers its own set of**Attributes**with the*probability distribution*parameters.Attribute | Type | Description |
---|---|---|

`Distribution` | Drop-down | The probability distribution that the random generator will use. |

Next, the

**Attributes**for each*probability distribution*are described. For each*probability distribution*, the link to its corresponding Wikipedia entry is given.*Probability distribution*of a

*random variable*that can take two values:

*true*, with probability p; and

*false*, with probability 1-p. When this distribution is chosen, the outcome of the

**Node**is a

**Boolean**.

Attribute | Type | Description |
---|---|---|

`Probability of 'true'` | Float (between 0 and 1) | The probability that the outcome will be true. |

*Probability distribution*of the number of successes in a sequence of independent experiment, each one with two possible outcomes: success and failure. The parameters for this

*probability distribution*are the number of experiments and the probability of a successful outcome in each one.

Attribute | Type | Description |
---|---|---|

`Data Type` | Drop-down | Whether the outcome will be an Int or Byte. |

`Probability of 'true'` | Float | The probability that the outcome of each trial is true. |

`Number of trials` | Int | The number of independent experiments performed, each one with probability of success `Probability of 'true'` . |

*Probability distribution*that resembles a

*normal*distribution but with a taller peak, whose tails decay slower. Its parameters are the location of the peak and the scale - the latter defines its width.

Attribute | Type | Description |
---|---|---|

`Location` | Float | Defines where the peak is. |

`Scale` | Float | Half the width of the probability density function at half the maximum height. |

*Probability distribution*of a sum of the squares of a number of independent normal

*random variables*. The number of normal

*random variables*is called the degrees of freedom of the Chi-squared

*distribution*.

Attribute | Type | Description |
---|---|---|

`Degrees of freedom` | Float | Number of independent normal random variables that are summed. |

*Probability distribution*of the time between events in a Poisson process. Its parameter is the rate at which the events in the Poisson process occur.

Attribute | Type | Description |
---|---|---|

`Rate` | Float | Rate at which the events in the Poisson process occur. |

Limit distribution of properly normalized maxima of a sequence of independent and identically distributed

*random variables*.Attribute | Type | Description |
---|---|---|

`Location` | Float | Defines where the peak is. |

`Scale` | Float | Defines how spread out the values are. |

Ratio of two independent

*random variables*with chi-squared distributions, each one divided by its corresponding number of degrees of freedom for scaling.Attribute | Type | Description |
---|---|---|

`Denominator Dof` | Float | Degrees of freedom of the chi-squared random variable in the denominator. |

`Numerator DoF` | Float | Degrees of freedom of the chi-squared random variable in the numerator. |

Maximum entropy probability distribution for a

*random variable*, whose mean is the product between the shape and scale, which are the two parameters of the Gamma distribution.Attribute | Type | Description |
---|---|---|

`Shape` | Float | Modifies the shape of the probability distribution. |

`Scale` | Float | Defines how spread out are the values. |

The probability distribution of the number of experiments with a Bernoulli distribution needed to get one success.

Attribute | Type | Description |
---|---|---|

`Data Type` | Drop-down | Whether the output is an Int or Byte. |

`Probability of 'true'` | Float (between 0 and 1) | The probability of success in the Bernoulli trials. |

Probability distribution of a

*random variable*whose logarithm has a normal distribution.Attribute | Type | Description |
---|---|---|

`Mean` | Float | The mean value of the logarithm of the distribution. |

`Standard deviation` | Float | The standard deviation of the logarithm of the distribution. |

*Probability distribution*of the number of successes in a sequence of independent experiments, each with two possible outcomes: success and failure, before a specified non-random number of failures occur. The parameters for this

*probability distribution*are the probability of a successful outcome in each experiment and the number of failures until the experiments stop.

Attribute | Type | Description |
---|---|---|

`Data Type` | Drop-down | Whether the outcome is an Int or Byte. |

`Probability of 'true'` | Float (between 0 and 1) | The probability that the outcome of each trial is true. |

`Number of trials` | Int | The number of failures to occur until the experiments stop. |

Symmetric

*probability distribution*, with half its values less than the mean and half greater than the mean. The parameters are the mean, which equals the median and the mode, and the standard deviation.Attribute | Type | Description |
---|---|---|

`Mean` | Float | The mean value of the distribution. |

`Standard deviation` | Float | The standard deviation of the distribution. |

Discrete

*probability distribution*that expresses the probability of a given number of events occurring in a specified time period. Its parameter is the mean value.Attribute | Type | Description |
---|---|---|

`Data Type` | Drop-down | Wheter the outcome will be an Int or Byte. |

`Mean` | Float | The mean value of the distribution. |

*Probability distribution*that arises when estimating the mean of a normally-distributed statistical population with a small sample size and unknown standard deviation. Its parameter is the number of degrees of freedom, which is the number of observations taken from a normal distribution minus one.

Attribute | Type | Description |
---|---|---|

`Degrees of freedom` | Float | The number of observations taken from a normal distribution minus one. As it grows, the Student-t distribution approaches a normal distribution. |

- Uniform

*Probability distribution*in which all the values in an interval are equally likely to be drawn. It can either be continuous or discrete.

Attribute | Type | Description |
---|---|---|

`Data Type` | Drop-down | Whether an Int, Float, or Byte will be generated. |

`Minimum` | Defined in the
Attribute | The lower bound of the interval from which the random number will be extracted. |

`Maximum` | Defined in the
Attribute | The upper bound of the interval from which the random number will be extracted. |

Attribute | Type | Description |
---|---|---|

`Shape` | Float | Defines the shape of the probability distribution. |

`Scale` | Float | Defines how spread out the values of the probability distribution are. |

Attribute | Type | Description |
---|---|---|

`Data Type` | Drop-down | Whether an Int, Float, or Byte will be generated. |

`Minimum` | Defined in the
Attribute | The lower bound of the interval from which the random number will be extracted. |

`Maximum` | Defined in the
Attribute | The upper bound of the interval from which the random number will be extracted. |

Input | Type | Description |
---|---|---|

Pulse Input (►) | Pulse | A standard Input Pulse, to trigger the execution of the Node. |

Output | Type | Description |
---|---|---|

Pulse Output (►) | Pulse | A standard Output Pulse, to move onto the next Node along the Logic Branch, once this Node has finished its execution. |

`Output` | Depends on the
and
| The random outcome that was generated. |

Last modified 3mo ago